Overview

Dataset statistics

Number of variables19
Number of observations1800
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory267.3 KiB
Average record size in memory152.1 B

Variable types

Numeric18
Categorical1

Alerts

Nombre_Municipio has a high cardinality: 72 distinct valuesHigh cardinality
Unnamed: 0 is highly overall correlated with Año and 1 other fieldsHigh correlation
Año is highly overall correlated with Unnamed: 0 and 1 other fieldsHigh correlation
Id_Municipio is highly overall correlated with Nombre_MunicipioHigh correlation
Poblacion is highly overall correlated with Nombre_Municipio and 7 other fieldsHigh correlation
Total_Accidentes is highly overall correlated with Nombre_Municipio and 7 other fieldsHigh correlation
Tasa_Accidentes is highly overall correlated with Nombre_Municipio and 4 other fieldsHigh correlation
Total_Muertes is highly overall correlated with Nombre_Municipio and 6 other fieldsHigh correlation
Tasa_Muertos is highly overall correlated with Tasa_Accidentes and 4 other fieldsHigh correlation
Total_Muertes_Alcohol is highly overall correlated with Nombre_Municipio and 5 other fieldsHigh correlation
Tasa_Muertos_Alcohol is highly overall correlated with Tasa_Muertos and 2 other fieldsHigh correlation
Total_Heridos is highly overall correlated with Nombre_Municipio and 6 other fieldsHigh correlation
Tasa_Heridos is highly overall correlated with Tasa_Accidentes and 4 other fieldsHigh correlation
Total_Heridos_Alcohol is highly overall correlated with Nombre_Municipio and 5 other fieldsHigh correlation
Tasa_Heridos_Alcohol is highly overall correlated with Tasa_Muertos and 3 other fieldsHigh correlation
Total_Cinturon_Uso is highly overall correlated with Poblacion and 4 other fieldsHigh correlation
Total_Cinturon_Tasa is highly overall correlated with Nombre_Municipio and 6 other fieldsHigh correlation
Total_Fugas is highly overall correlated with Nombre_Municipio and 5 other fieldsHigh correlation
Total_Fugas_Tasa is highly overall correlated with Unnamed: 0 and 3 other fieldsHigh correlation
Nombre_Municipio is highly overall correlated with Id_Municipio and 10 other fieldsHigh correlation
Unnamed: 0 is uniformly distributedUniform
Nombre_Municipio is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
Total_Accidentes has 254 (14.1%) zerosZeros
Tasa_Accidentes has 254 (14.1%) zerosZeros
Total_Muertes has 894 (49.7%) zerosZeros
Tasa_Muertos has 894 (49.7%) zerosZeros
Total_Muertes_Alcohol has 1366 (75.9%) zerosZeros
Tasa_Muertos_Alcohol has 1366 (75.9%) zerosZeros
Total_Heridos has 528 (29.3%) zerosZeros
Tasa_Heridos has 528 (29.3%) zerosZeros
Total_Heridos_Alcohol has 871 (48.4%) zerosZeros
Tasa_Heridos_Alcohol has 871 (48.4%) zerosZeros
Total_Cinturon_Uso has 997 (55.4%) zerosZeros
Total_Cinturon_Tasa has 997 (55.4%) zerosZeros
Total_Fugas has 1009 (56.1%) zerosZeros
Total_Fugas_Tasa has 1009 (56.1%) zerosZeros

Reproduction

Analysis started2022-12-12 22:39:37.046580
Analysis finished2022-12-12 22:41:10.787580
Duration1 minute and 33.74 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1800
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean899.5
Minimum0
Maximum1799
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2022-12-12T15:41:11.008576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile89.95
Q1449.75
median899.5
Q31349.25
95-th percentile1709.05
Maximum1799
Range1799
Interquartile range (IQR)899.5

Descriptive statistics

Standard deviation519.75956
Coefficient of variation (CV)0.57783164
Kurtosis-1.2
Mean899.5
Median Absolute Deviation (MAD)450
Skewness0
Sum1619100
Variance270150
MonotonicityStrictly increasing
2022-12-12T15:41:11.240337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.1%
1237 1
 
0.1%
1207 1
 
0.1%
1206 1
 
0.1%
1205 1
 
0.1%
1204 1
 
0.1%
1203 1
 
0.1%
1202 1
 
0.1%
1201 1
 
0.1%
1200 1
 
0.1%
Other values (1790) 1790
99.4%
ValueCountFrequency (%)
0 1
0.1%
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
ValueCountFrequency (%)
1799 1
0.1%
1798 1
0.1%
1797 1
0.1%
1796 1
0.1%
1795 1
0.1%
1794 1
0.1%
1793 1
0.1%
1792 1
0.1%
1791 1
0.1%
1790 1
0.1%

Año
Real number (ℝ)

Distinct25
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009
Minimum1997
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2022-12-12T15:41:11.468332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile1998
Q12003
median2009
Q32015
95-th percentile2020
Maximum2021
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.2131065
Coefficient of variation (CV)0.0035903965
Kurtosis-1.2038561
Mean2009
Median Absolute Deviation (MAD)6
Skewness0
Sum3616200
Variance52.028905
MonotonicityIncreasing
2022-12-12T15:41:11.862403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1997 72
 
4.0%
2010 72
 
4.0%
2020 72
 
4.0%
2019 72
 
4.0%
2018 72
 
4.0%
2017 72
 
4.0%
2016 72
 
4.0%
2015 72
 
4.0%
2014 72
 
4.0%
2013 72
 
4.0%
Other values (15) 1080
60.0%
ValueCountFrequency (%)
1997 72
4.0%
1998 72
4.0%
1999 72
4.0%
2000 72
4.0%
2001 72
4.0%
2002 72
4.0%
2003 72
4.0%
2004 72
4.0%
2005 72
4.0%
2006 72
4.0%
ValueCountFrequency (%)
2021 72
4.0%
2020 72
4.0%
2019 72
4.0%
2018 72
4.0%
2017 72
4.0%
2016 72
4.0%
2015 72
4.0%
2014 72
4.0%
2013 72
4.0%
2012 72
4.0%

Id_Municipio
Real number (ℝ)

Distinct72
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.5
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2022-12-12T15:41:12.483978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q118.75
median36.5
Q354.25
95-th percentile69
Maximum72
Range71
Interquartile range (IQR)35.5

Descriptive statistics

Standard deviation20.78838
Coefficient of variation (CV)0.56954466
Kurtosis-1.2004636
Mean36.5
Median Absolute Deviation (MAD)18
Skewness0
Sum65700
Variance432.15675
MonotonicityNot monotonic
2022-12-12T15:41:13.104974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 25
 
1.4%
2 25
 
1.4%
53 25
 
1.4%
52 25
 
1.4%
51 25
 
1.4%
50 25
 
1.4%
49 25
 
1.4%
48 25
 
1.4%
47 25
 
1.4%
46 25
 
1.4%
Other values (62) 1550
86.1%
ValueCountFrequency (%)
1 25
1.4%
2 25
1.4%
3 25
1.4%
4 25
1.4%
5 25
1.4%
6 25
1.4%
7 25
1.4%
8 25
1.4%
9 25
1.4%
10 25
1.4%
ValueCountFrequency (%)
72 25
1.4%
71 25
1.4%
70 25
1.4%
69 25
1.4%
68 25
1.4%
67 25
1.4%
66 25
1.4%
65 25
1.4%
64 25
1.4%
63 25
1.4%

Nombre_Municipio
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM

Distinct72
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size14.2 KiB
Aconchi
 
25
Agua Prieta
 
25
San Felipe de Jesús
 
25
Sahuaripa
 
25
Rosario
 
25
Other values (67)
1675 

Length

Max length29
Median length21
Mean length9.5
Min length4

Characters and Unicode

Total characters17100
Distinct characters51
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAconchi
2nd rowAgua Prieta
3rd rowAlamos
4th rowAltar
5th rowArivechi

Common Values

ValueCountFrequency (%)
Aconchi 25
 
1.4%
Agua Prieta 25
 
1.4%
San Felipe de Jesús 25
 
1.4%
Sahuaripa 25
 
1.4%
Rosario 25
 
1.4%
Rayón 25
 
1.4%
Quiriego 25
 
1.4%
Puerto Peñasco 25
 
1.4%
Pitiquito 25
 
1.4%
Oquitoa 25
 
1.4%
Other values (62) 1550
86.1%

Length

2022-12-12T15:41:13.357969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san 150
 
5.7%
de 100
 
3.8%
santa 50
 
1.9%
la 50
 
1.9%
río 50
 
1.9%
villa 50
 
1.9%
aconchi 25
 
1.0%
baviácora 25
 
1.0%
atil 25
 
1.0%
bacadéhuachi 25
 
1.0%
Other values (83) 2075
79.0%

Most occurring characters

ValueCountFrequency (%)
a 2575
15.1%
e 1175
 
6.9%
o 1175
 
6.9%
i 1075
 
6.3%
r 1025
 
6.0%
825
 
4.8%
c 800
 
4.7%
u 750
 
4.4%
n 700
 
4.1%
s 650
 
3.8%
Other values (41) 6350
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13775
80.6%
Uppercase Letter 2500
 
14.6%
Space Separator 825
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2575
18.7%
e 1175
 
8.5%
o 1175
 
8.5%
i 1075
 
7.8%
r 1025
 
7.4%
c 800
 
5.8%
u 750
 
5.4%
n 700
 
5.1%
s 650
 
4.7%
l 625
 
4.5%
Other values (18) 3225
23.4%
Uppercase Letter
ValueCountFrequency (%)
S 300
12.0%
C 300
12.0%
B 250
10.0%
H 200
 
8.0%
A 200
 
8.0%
P 175
 
7.0%
M 125
 
5.0%
N 125
 
5.0%
G 125
 
5.0%
R 100
 
4.0%
Other values (12) 600
24.0%
Space Separator
ValueCountFrequency (%)
825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16275
95.2%
Common 825
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2575
15.8%
e 1175
 
7.2%
o 1175
 
7.2%
i 1075
 
6.6%
r 1025
 
6.3%
c 800
 
4.9%
u 750
 
4.6%
n 700
 
4.3%
s 650
 
4.0%
l 625
 
3.8%
Other values (40) 5725
35.2%
Common
ValueCountFrequency (%)
825
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16600
97.1%
None 500
 
2.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2575
15.5%
e 1175
 
7.1%
o 1175
 
7.1%
i 1075
 
6.5%
r 1025
 
6.2%
825
 
5.0%
c 800
 
4.8%
u 750
 
4.5%
n 700
 
4.2%
s 650
 
3.9%
Other values (35) 5850
35.2%
None
ValueCountFrequency (%)
á 200
40.0%
í 125
25.0%
é 75
 
15.0%
ó 50
 
10.0%
ñ 25
 
5.0%
ú 25
 
5.0%

Poblacion
Real number (ℝ)

Distinct211
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34075.043
Minimum279
Maximum936263
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2022-12-12T15:41:13.587972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum279
5-th percentile492
Q11454
median3396
Q314365
95-th percentile157729
Maximum936263
Range935984
Interquartile range (IQR)12911

Descriptive statistics

Standard deviation99457.403
Coefficient of variation (CV)2.9187756
Kurtosis33.195767
Mean34075.043
Median Absolute Deviation (MAD)2571
Skewness5.3591128
Sum61335077
Variance9.891775 × 109
MonotonicityNot monotonic
2022-12-12T15:41:13.807920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1484 26
 
1.4%
1350 20
 
1.1%
1646 20
 
1.1%
1496 15
 
0.8%
943 15
 
0.8%
3335 13
 
0.7%
5626 13
 
0.7%
279 13
 
0.7%
416 13
 
0.7%
6400 13
 
0.7%
Other values (201) 1639
91.1%
ValueCountFrequency (%)
279 13
0.7%
365 2
 
0.1%
369 2
 
0.1%
396 10
0.6%
399 10
0.6%
402 13
0.7%
416 13
0.7%
443 10
0.6%
479 13
0.7%
492 10
0.6%
ValueCountFrequency (%)
936263 2
 
0.1%
784342 10
0.6%
609829 13
0.7%
436484 2
 
0.1%
409310 10
0.6%
356290 13
0.7%
264782 2
 
0.1%
220292 10
0.6%
199021 2
 
0.1%
178380 10
0.6%

Total_Accidentes
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct398
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215.46667
Minimum0
Maximum8693
Zeros254
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2022-12-12T15:41:14.049920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q361.25
95-th percentile1343.05
Maximum8693
Range8693
Interquartile range (IQR)58.25

Descriptive statistics

Standard deviation671.34511
Coefficient of variation (CV)3.1157725
Kurtosis32.203927
Mean215.46667
Median Absolute Deviation (MAD)12
Skewness5.0385323
Sum387840
Variance450704.26
MonotonicityNot monotonic
2022-12-12T15:41:14.274943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 254
 
14.1%
12 161
 
8.9%
1 97
 
5.4%
2 68
 
3.8%
3 59
 
3.3%
5 58
 
3.2%
4 52
 
2.9%
6 44
 
2.4%
8 40
 
2.2%
7 31
 
1.7%
Other values (388) 936
52.0%
ValueCountFrequency (%)
0 254
14.1%
1 97
 
5.4%
2 68
 
3.8%
3 59
 
3.3%
4 52
 
2.9%
5 58
 
3.2%
6 44
 
2.4%
7 31
 
1.7%
8 40
 
2.2%
9 29
 
1.6%
ValueCountFrequency (%)
8693 1
0.1%
5157 1
0.1%
5005 1
0.1%
4790 1
0.1%
4612 1
0.1%
4462 1
0.1%
4435 1
0.1%
4430 1
0.1%
4393 1
0.1%
4390 1
0.1%

Tasa_Accidentes
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct1250
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0334616
Minimum0
Maximum37.313433
Zeros254
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2022-12-12T15:41:14.539939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.2891918
median3.7252757
Q37.2248812
95-th percentile14.966261
Maximum37.313433
Range37.313433
Interquartile range (IQR)5.9356893

Descriptive statistics

Standard deviation5.1760902
Coefficient of variation (CV)1.0283361
Kurtosis5.5859836
Mean5.0334616
Median Absolute Deviation (MAD)2.7511022
Skewness1.9582689
Sum9060.2308
Variance26.79191
MonotonicityNot monotonic
2022-12-12T15:41:14.834938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 254
 
14.1%
5.028284098 5
 
0.3%
2.087682672 5
 
0.3%
5.336179296 5
 
0.3%
4.043126685 4
 
0.2%
0.4336513443 4
 
0.2%
12.47401247 4
 
0.2%
1.751313485 4
 
0.2%
8.021390374 4
 
0.2%
1.212856277 4
 
0.2%
Other values (1240) 1507
83.7%
ValueCountFrequency (%)
0 254
14.1%
0.04689991558 1
 
0.1%
0.1001201442 1
 
0.1%
0.1193032689 1
 
0.1%
0.1647717911 1
 
0.1%
0.1895734597 1
 
0.1%
0.2006420546 1
 
0.1%
0.2979737783 2
 
0.1%
0.299850075 1
 
0.1%
0.3206898395 1
 
0.1%
ValueCountFrequency (%)
37.31343284 1
 
0.1%
32.87671233 2
0.1%
32.59259259 1
 
0.1%
32.5203252 2
0.1%
30.3030303 2
0.1%
30.07518797 3
0.2%
29.34537246 1
 
0.1%
27.08803612 2
0.1%
25.06265664 1
 
0.1%
24.87562189 1
 
0.1%

Total_Muertes
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct57
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6605556
Minimum0
Maximum180
Zeros894
Zeros (%)49.7%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2022-12-12T15:41:15.113944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile19
Maximum180
Range180
Interquartile range (IQR)3

Descriptive statistics

Standard deviation9.2275176
Coefficient of variation (CV)2.520797
Kurtosis88.569314
Mean3.6605556
Median Absolute Deviation (MAD)1
Skewness6.9528838
Sum6589
Variance85.147081
MonotonicityNot monotonic
2022-12-12T15:41:15.384939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 894
49.7%
1 252
 
14.0%
2 166
 
9.2%
3 84
 
4.7%
4 62
 
3.4%
5 49
 
2.7%
6 34
 
1.9%
7 25
 
1.4%
8 22
 
1.2%
9 20
 
1.1%
Other values (47) 192
 
10.7%
ValueCountFrequency (%)
0 894
49.7%
1 252
 
14.0%
2 166
 
9.2%
3 84
 
4.7%
4 62
 
3.4%
5 49
 
2.7%
6 34
 
1.9%
7 25
 
1.4%
8 22
 
1.2%
9 20
 
1.1%
ValueCountFrequency (%)
180 1
0.1%
80 1
0.1%
75 2
0.1%
67 1
0.1%
65 1
0.1%
62 1
0.1%
61 1
0.1%
55 1
0.1%
54 2
0.1%
53 1
0.1%

Tasa_Muertos
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct574
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22562231
Minimum0
Maximum10.204082
Zeros894
Zeros (%)49.7%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2022-12-12T15:41:15.669467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.016979858
Q30.21123785
95-th percentile1.0600732
Maximum10.204082
Range10.204082
Interquartile range (IQR)0.21123785

Descriptive statistics

Standard deviation0.57664477
Coefficient of variation (CV)2.5557967
Kurtosis102.84306
Mean0.22562231
Median Absolute Deviation (MAD)0.016979858
Skewness8.0670968
Sum406.12016
Variance0.33251919
MonotonicityNot monotonic
2022-12-12T15:41:15.933467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 894
49.7%
0.1840942563 9
 
0.5%
0.6075334143 7
 
0.4%
0.2757479664 6
 
0.3%
0.07393168712 5
 
0.3%
0.1392272885 5
 
0.3%
1.481481481 5
 
0.3%
0.6142506143 5
 
0.3%
0.2137932263 5
 
0.3%
0.1862197393 5
 
0.3%
Other values (564) 854
47.4%
ValueCountFrequency (%)
0 894
49.7%
0.004582069446 1
 
0.1%
0.006873104169 1
 
0.1%
0.01251666281 1
 
0.1%
0.01263615457 1
 
0.1%
0.01339593701 1
 
0.1%
0.01614361359 1
 
0.1%
0.01781610219 1
 
0.1%
0.02000520135 2
 
0.1%
0.02009390552 1
 
0.1%
ValueCountFrequency (%)
10.20408163 1
0.1%
9.029345372 1
0.1%
7.462686567 1
0.1%
5.050505051 1
0.1%
4.975124378 1
0.1%
4.175365344 1
0.1%
4.032258065 1
0.1%
3.584229391 1
0.1%
2.90486565 2
0.1%
2.791346825 1
0.1%

Total_Muertes_Alcohol
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct13
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52333333
Minimum0
Maximum18
Zeros1366
Zeros (%)75.9%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2022-12-12T15:41:16.164464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3380771
Coefficient of variation (CV)2.5568352
Kurtosis34.886616
Mean0.52333333
Median Absolute Deviation (MAD)0
Skewness4.7874853
Sum942
Variance1.7904503
MonotonicityNot monotonic
2022-12-12T15:41:16.378763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 1366
75.9%
1 218
 
12.1%
2 112
 
6.2%
3 36
 
2.0%
4 31
 
1.7%
5 11
 
0.6%
6 10
 
0.6%
7 4
 
0.2%
9 3
 
0.2%
10 3
 
0.2%
Other values (3) 6
 
0.3%
ValueCountFrequency (%)
0 1366
75.9%
1 218
 
12.1%
2 112
 
6.2%
3 36
 
2.0%
4 31
 
1.7%
5 11
 
0.6%
6 10
 
0.6%
7 4
 
0.2%
8 3
 
0.2%
9 3
 
0.2%
ValueCountFrequency (%)
18 1
 
0.1%
13 2
 
0.1%
10 3
 
0.2%
9 3
 
0.2%
8 3
 
0.2%
7 4
 
0.2%
6 10
 
0.6%
5 11
 
0.6%
4 31
1.7%
3 36
2.0%

Tasa_Muertos_Alcohol
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct257
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.064876429
Minimum0
Maximum6.772009
Zeros1366
Zeros (%)75.9%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2022-12-12T15:41:16.665464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.33086866
Maximum6.772009
Range6.772009
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.29878806
Coefficient of variation (CV)4.6054949
Kurtosis195.04314
Mean0.064876429
Median Absolute Deviation (MAD)0
Skewness11.389324
Sum116.77757
Variance0.089274305
MonotonicityNot monotonic
2022-12-12T15:41:16.959991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1366
75.9%
0.01310684702 10
 
0.6%
0.6075334143 8
 
0.4%
0.06419103251 6
 
0.3%
0.03119054303 5
 
0.3%
0.01438517751 5
 
0.3%
0.01263615457 5
 
0.3%
0.15625 5
 
0.3%
0.1105094486 4
 
0.2%
0.01251666281 4
 
0.2%
Other values (247) 382
 
21.2%
ValueCountFrequency (%)
0 1366
75.9%
0.001274954038 1
 
0.1%
0.002136151915 1
 
0.1%
0.002443136009 1
 
0.1%
0.002549908076 1
 
0.1%
0.002806702405 3
 
0.2%
0.00327960789 2
 
0.1%
0.003824862114 1
 
0.1%
0.004539429485 3
 
0.2%
0.004886272019 1
 
0.1%
ValueCountFrequency (%)
6.772009029 1
0.1%
4.975124378 1
0.1%
2.487562189 1
0.1%
2.424242424 1
0.1%
2.257336343 2
0.1%
2.087682672 1
0.1%
2.079002079 1
0.1%
2.070393375 1
0.1%
1.766784452 1
0.1%
1.597444089 1
0.1%

Total_Heridos
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct315
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.795
Minimum0
Maximum3894
Zeros528
Zeros (%)29.3%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2022-12-12T15:41:17.237995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q327.25
95-th percentile568.1
Maximum3894
Range3894
Interquartile range (IQR)27.25

Descriptive statistics

Standard deviation328.25288
Coefficient of variation (CV)3.3912173
Kurtosis45.943497
Mean96.795
Median Absolute Deviation (MAD)5
Skewness6.0975732
Sum174231
Variance107749.95
MonotonicityNot monotonic
2022-12-12T15:41:17.483990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 528
29.3%
1 120
 
6.7%
2 110
 
6.1%
4 69
 
3.8%
3 67
 
3.7%
5 56
 
3.1%
6 48
 
2.7%
9 32
 
1.8%
7 32
 
1.8%
8 29
 
1.6%
Other values (305) 709
39.4%
ValueCountFrequency (%)
0 528
29.3%
1 120
 
6.7%
2 110
 
6.1%
3 67
 
3.7%
4 69
 
3.8%
5 56
 
3.1%
6 48
 
2.7%
7 32
 
1.8%
8 29
 
1.6%
9 32
 
1.8%
ValueCountFrequency (%)
3894 1
0.1%
3711 1
0.1%
3125 1
0.1%
3103 1
0.1%
3021 1
0.1%
2895 1
0.1%
2735 1
0.1%
2548 1
0.1%
2503 1
0.1%
2318 1
0.1%

Tasa_Heridos
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct1053
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.147162
Minimum0
Maximum64.676617
Zeros528
Zeros (%)29.3%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2022-12-12T15:41:17.736989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.2150668
Q32.8260116
95-th percentile6.9637883
Maximum64.676617
Range64.676617
Interquartile range (IQR)2.8260116

Descriptive statistics

Standard deviation3.6526968
Coefficient of variation (CV)1.7011743
Kurtosis72.087031
Mean2.147162
Median Absolute Deviation (MAD)1.2150668
Skewness6.4095979
Sum3864.8917
Variance13.342194
MonotonicityNot monotonic
2022-12-12T15:41:17.949989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 528
29.3%
1.215066829 6
 
0.3%
0.7320644217 5
 
0.3%
0.537056928 5
 
0.3%
0.8718395815 5
 
0.3%
1.271364161 4
 
0.2%
2.673796791 4
 
0.2%
2.69541779 4
 
0.2%
0.3791469194 4
 
0.2%
0.8756567426 4
 
0.2%
Other values (1043) 1231
68.4%
ValueCountFrequency (%)
0 528
29.3%
0.02574599006 1
 
0.1%
0.07338372349 1
 
0.1%
0.07737542556 1
 
0.1%
0.09379983116 1
 
0.1%
0.1082719792 2
 
0.1%
0.1281886938 1
 
0.1%
0.1378739832 2
 
0.1%
0.1562255898 1
 
0.1%
0.1612383102 1
 
0.1%
ValueCountFrequency (%)
64.67661692 1
0.1%
40 1
0.1%
39.80099502 1
0.1%
39.25925926 1
0.1%
25.25252525 1
0.1%
24.83069977 2
0.1%
23.4791889 1
0.1%
22.55639098 1
0.1%
21.63461538 1
0.1%
20.83333333 1
0.1%

Total_Heridos_Alcohol
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct147
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.117222
Minimum0
Maximum959
Zeros871
Zeros (%)48.4%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2022-12-12T15:41:18.340991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile78.05
Maximum959
Range959
Interquartile range (IQR)6

Descriptive statistics

Standard deviation61.128639
Coefficient of variation (CV)3.7927528
Kurtosis92.145956
Mean16.117222
Median Absolute Deviation (MAD)1
Skewness8.4341049
Sum29011
Variance3736.7105
MonotonicityNot monotonic
2022-12-12T15:41:18.895997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 871
48.4%
1 143
 
7.9%
2 113
 
6.3%
3 90
 
5.0%
4 54
 
3.0%
5 49
 
2.7%
6 42
 
2.3%
7 33
 
1.8%
8 30
 
1.7%
9 23
 
1.3%
Other values (137) 352
19.6%
ValueCountFrequency (%)
0 871
48.4%
1 143
 
7.9%
2 113
 
6.3%
3 90
 
5.0%
4 54
 
3.0%
5 49
 
2.7%
6 42
 
2.3%
7 33
 
1.8%
8 30
 
1.7%
9 23
 
1.3%
ValueCountFrequency (%)
959 1
0.1%
864 1
0.1%
763 1
0.1%
627 1
0.1%
610 1
0.1%
604 1
0.1%
561 1
0.1%
537 1
0.1%
508 1
0.1%
499 1
0.1%

Tasa_Heridos_Alcohol
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct750
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62103672
Minimum0
Maximum19.259259
Zeros871
Zeros (%)48.4%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2022-12-12T15:41:19.565993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.045415126
Q30.65063918
95-th percentile2.6954178
Maximum19.259259
Range19.259259
Interquartile range (IQR)0.65063918

Descriptive statistics

Standard deviation1.5654563
Coefficient of variation (CV)2.5207146
Kurtosis50.697881
Mean0.62103672
Median Absolute Deviation (MAD)0.045415126
Skewness6.144043
Sum1117.8661
Variance2.4506534
MonotonicityNot monotonic
2022-12-12T15:41:19.818991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 871
48.4%
0.6075334143 7
 
0.4%
0.7320644217 4
 
0.2%
0.8861320337 4
 
0.2%
0.8944543828 4
 
0.2%
0.7407407407 4
 
0.2%
0.268528464 4
 
0.2%
0.2388344877 3
 
0.2%
0.5617977528 3
 
0.2%
0.2002402883 3
 
0.2%
Other values (740) 893
49.6%
ValueCountFrequency (%)
0 871
48.4%
0.007329408028 1
 
0.1%
0.01267997642 3
 
0.2%
0.01339593701 1
 
0.1%
0.0174392243 1
 
0.1%
0.01824961828 1
 
0.1%
0.01954508808 1
 
0.1%
0.02512297697 2
 
0.1%
0.02803004821 1
 
0.1%
0.03036191402 1
 
0.1%
ValueCountFrequency (%)
19.25925926 1
0.1%
18.05869074 1
0.1%
17.41293532 1
0.1%
16.82692308 1
0.1%
15.15151515 1
0.1%
14.49275362 1
0.1%
12.62626263 1
0.1%
12.43781095 1
0.1%
11.47959184 1
0.1%
11.28668172 1
0.1%

Total_Cinturon_Uso
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct129
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.865556
Minimum0
Maximum7367
Zeros997
Zeros (%)55.4%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2022-12-12T15:41:20.191990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile54.05
Maximum7367
Range7367
Interquartile range (IQR)2

Descriptive statistics

Standard deviation283.78326
Coefficient of variation (CV)7.3016648
Kurtosis288.63108
Mean38.865556
Median Absolute Deviation (MAD)0
Skewness14.465297
Sum69958
Variance80532.939
MonotonicityNot monotonic
2022-12-12T15:41:20.479990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 997
55.4%
1 226
 
12.6%
2 140
 
7.8%
3 84
 
4.7%
4 41
 
2.3%
5 33
 
1.8%
6 27
 
1.5%
8 16
 
0.9%
13 12
 
0.7%
7 12
 
0.7%
Other values (119) 212
 
11.8%
ValueCountFrequency (%)
0 997
55.4%
1 226
 
12.6%
2 140
 
7.8%
3 84
 
4.7%
4 41
 
2.3%
5 33
 
1.8%
6 27
 
1.5%
7 12
 
0.7%
8 16
 
0.9%
9 11
 
0.6%
ValueCountFrequency (%)
7367 1
0.1%
3316 1
0.1%
2975 1
0.1%
2845 1
0.1%
2724 1
0.1%
2456 1
0.1%
2440 1
0.1%
2339 1
0.1%
2247 1
0.1%
2217 1
0.1%

Total_Cinturon_Tasa
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct588
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58112096
Minimum0
Maximum16.677879
Zeros997
Zeros (%)55.4%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2022-12-12T15:41:20.791995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.48736339
95-th percentile3.1111067
Maximum16.677879
Range16.677879
Interquartile range (IQR)0.48736339

Descriptive statistics

Standard deviation1.4925317
Coefficient of variation (CV)2.5683667
Kurtosis38.445754
Mean0.58112096
Median Absolute Deviation (MAD)0
Skewness5.2775828
Sum1046.0177
Variance2.227651
MonotonicityNot monotonic
2022-12-12T15:41:21.036084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 997
55.4%
0.8718395815 6
 
0.3%
0.08119519324 4
 
0.2%
2.525252525 4
 
0.2%
0.1571832757 4
 
0.2%
0.4875670405 4
 
0.2%
0.2177463255 4
 
0.2%
0.1612383102 4
 
0.2%
1.215066829 4
 
0.2%
0.2808988764 4
 
0.2%
Other values (578) 765
42.5%
ValueCountFrequency (%)
0 997
55.4%
0.002443136009 1
 
0.1%
0.002806702405 1
 
0.1%
0.003776691769 1
 
0.1%
0.004539429485 1
 
0.1%
0.005613404811 1
 
0.1%
0.006339988208 1
 
0.1%
0.007329408028 1
 
0.1%
0.00767288938 1
 
0.1%
0.009078858969 3
 
0.2%
ValueCountFrequency (%)
16.67787869 1
0.1%
15.94909743 1
0.1%
15.27077027 1
0.1%
14.92537313 1
0.1%
14.07407407 1
0.1%
12.59259259 1
0.1%
12.34040629 1
0.1%
11.73959445 1
0.1%
11.13952799 1
0.1%
10.02506266 1
0.1%

Total_Fugas
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct177
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.787778
Minimum0
Maximum2176
Zeros1009
Zeros (%)56.1%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2022-12-12T15:41:21.297823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39.25
95-th percentile154.4
Maximum2176
Range2176
Interquartile range (IQR)9.25

Descriptive statistics

Standard deviation100.49868
Coefficient of variation (CV)4.0543643
Kurtosis166.1693
Mean24.787778
Median Absolute Deviation (MAD)0
Skewness10.530983
Sum44618
Variance10099.985
MonotonicityNot monotonic
2022-12-12T15:41:21.525954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1009
56.1%
12 107
 
5.9%
1 96
 
5.3%
2 54
 
3.0%
3 44
 
2.4%
11 36
 
2.0%
6 32
 
1.8%
10 29
 
1.6%
4 28
 
1.6%
8 27
 
1.5%
Other values (167) 338
 
18.8%
ValueCountFrequency (%)
0 1009
56.1%
1 96
 
5.3%
2 54
 
3.0%
3 44
 
2.4%
4 28
 
1.6%
5 26
 
1.4%
6 32
 
1.8%
7 19
 
1.1%
8 27
 
1.5%
9 15
 
0.8%
ValueCountFrequency (%)
2176 1
0.1%
1558 1
0.1%
949 1
0.1%
939 1
0.1%
881 1
0.1%
857 1
0.1%
786 1
0.1%
752 1
0.1%
742 1
0.1%
723 1
0.1%

Total_Fugas_Tasa
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct663
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1263939
Minimum0
Maximum32.876712
Zeros1009
Zeros (%)56.1%
Negative0
Negative (%)0.0%
Memory size14.2 KiB
2022-12-12T15:41:21.751958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.55009004
95-th percentile7.0087993
Maximum32.876712
Range32.876712
Interquartile range (IQR)0.55009004

Descriptive statistics

Standard deviation3.4315181
Coefficient of variation (CV)3.0464636
Kurtosis35.341204
Mean1.1263939
Median Absolute Deviation (MAD)0
Skewness5.3531269
Sum2027.509
Variance11.775316
MonotonicityNot monotonic
2022-12-12T15:41:21.978919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1009
56.1%
0.06244536031 4
 
0.2%
0.7407407407 3
 
0.2%
0.1160631383 3
 
0.2%
0.4983388704 3
 
0.2%
0.4543595802 3
 
0.2%
0.5005005005 3
 
0.2%
0.1612383102 3
 
0.2%
0.1913509376 3
 
0.2%
0.1157541382 3
 
0.2%
Other values (653) 763
42.4%
ValueCountFrequency (%)
0 1009
56.1%
0.004582069446 1
 
0.1%
0.005613404811 1
 
0.1%
0.006873104169 1
 
0.1%
0.006896266361 1
 
0.1%
0.008199019725 1
 
0.1%
0.01310684702 1
 
0.1%
0.01646985194 1
 
0.1%
0.01781610219 1
 
0.1%
0.02433282437 2
 
0.1%
ValueCountFrequency (%)
32.87671233 2
0.1%
32.5203252 2
0.1%
30.3030303 1
0.1%
30.07518797 2
0.1%
27.08803612 2
0.1%
24.3902439 2
0.1%
22.34636872 2
0.1%
22.17741935 2
0.1%
19.2 2
0.1%
19.16932907 1
0.1%

Interactions

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2022-12-12T15:40:44.315031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:48.962889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:53.869572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:57.678904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:41:02.254661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:41:08.453892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:39:49.939119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:39:54.438732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:39:58.511995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:03.698403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:08.162001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:12.200506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:17.006500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:22.351485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:27.434653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:31.157417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:35.799817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:40.458132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:44.534167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:49.197964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:54.082567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:57.889910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:41:02.564497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:41:08.663908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:39:50.149156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:39:54.633784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:39:58.732050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:03.892924image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:08.690230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:12.404158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:17.217724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:22.554490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:27.638652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:31.364411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:36.011816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:40.657253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:44.730088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:49.411016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:54.284575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:58.095958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:41:02.867491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:41:08.863566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:39:50.343157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:39:54.805312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:39:58.971576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:04.079947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:08.881847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:12.835156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:17.429855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:22.748489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:27.845153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:31.564412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:36.220942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:40.851836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:44.918601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:49.611996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:54.500566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:58.396955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:41:03.050491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:41:09.081759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:39:50.555290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:39:55.282485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:39:59.441571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:04.295087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:09.099080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:13.485680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:17.714854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:22.958016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:28.069684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:31.780597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:36.449542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:41.078908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:45.134707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:49.839998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:54.708569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:58.946965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:41:03.249493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:41:09.302584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:39:50.779345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:39:55.491486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:00.070644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:04.502976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:09.307180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:13.997680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:17.951843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:23.156017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:28.296740image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:31.991637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:36.666514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:41.289942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:45.537270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:50.065055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:54.930569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:59.500954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:41:03.447493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:41:09.502587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:39:50.979328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:39:55.691586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:00.691410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:04.715050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:09.536718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:14.212738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:18.191360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:23.362041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:28.512813image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:32.205643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:36.888578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:41.498932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:46.039802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:50.279997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:55.162570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:59.896971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:41:03.714493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:41:09.674612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:39:51.163074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:39:55.868764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:01.098401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:04.890052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:09.724782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:14.396778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:18.393363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:23.541144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:28.711957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:32.651215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:37.100728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:41.705933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:46.546807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:50.581997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:40:55.356571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:41:00.106952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-12T15:41:03.953492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-12T15:41:22.205965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-12T15:41:22.690967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-12T15:41:23.157261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-12T15:41:23.576263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-12T15:41:24.009311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-12T15:41:10.007577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-12T15:41:10.553578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0AñoId_MunicipioNombre_MunicipioPoblacionTotal_AccidentesTasa_AccidentesTotal_MuertesTasa_MuertosTotal_Muertes_AlcoholTasa_Muertos_AlcoholTotal_HeridosTasa_HeridosTotal_Heridos_AlcoholTasa_Heridos_AlcoholTotal_Cinturon_UsoTotal_Cinturon_TasaTotal_FugasTotal_Fugas_Tasa
0019971Aconchi242000.00000000.00000000.00000000.00000000.00000000.000.000000
1119972Agua Prieta6194400.00000000.00000000.00000000.00000000.00000000.000.000000
2219973Alamos25152431.70960640.15903310.039758210.83492460.23855000.050.198791
3319974Altar725391.24086600.00000000.00000010.13787400.00000000.020.275748
4419975Arivechi148421.34770910.67385400.00000000.00000000.00000000.000.000000
5519976Arizpe339682.35571320.58892800.00000051.47232010.29446400.000.000000
6619977Atil71800.00000000.00000000.00000000.00000000.00000000.000.000000
7719978Bacadéhuachi134800.00000000.00000000.00000000.00000000.00000000.000.000000
8819979Bacanora94300.00000000.00000000.00000000.00000000.00000000.000.000000
99199710Bacerac136600.00000000.00000000.00000000.00000000.00000000.000.000000
Unnamed: 0AñoId_MunicipioNombre_MunicipioPoblacionTotal_AccidentesTasa_AccidentesTotal_MuertesTasa_MuertosTotal_Muertes_AlcoholTasa_Muertos_AlcoholTotal_HeridosTasa_HeridosTotal_Heridos_AlcoholTasa_Heridos_AlcoholTotal_Cinturon_UsoTotal_Cinturon_TasaTotal_FugasTotal_Fugas_Tasa
17901790202163Tepache11781210.18675700.00000000.00000000.00000000.00000000.0000001210.186757
17911791202164Trincheras1381128.68935600.00000000.00000000.00000000.00000000.000000128.689356
17921792202165Tubutama1473128.14664000.00000000.00000000.00000000.00000000.000000128.146640
17931793202166Ures8548252.92466100.00000000.00000030.35095920.23397320.23397340.467946
17941794202167Villa Hidalgo1429128.39748110.69979000.00000000.00000000.00000000.000000117.697691
17951795202168Villa Pesqueira10431312.46404600.00000000.00000000.00000000.00000010.9587731211.505273
17961796202169Yécora4793132.71228900.00000000.00000000.00000000.00000030.625913102.086376
17971797202170General Plutarco Elías Calles13627604.40302310.07338400.00000010.07338400.00000030.220151110.807221
17981798202171Benito Juárez21692984.51779500.00000000.000000431.98229800.00000000.00000050.230500
17991799202172San Ignacio Río Muerto14279533.71174540.28013240.280132422.94138230.21009900.00000000.000000